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Documentation and consistency for deprecation #18928
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certainly take a PR on these
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@jreback, couple of questions: I'll make sure that the deprecation directive is in all cases right after the short description of the docstring, as the numpy docstring convention defines. Or at the start of the docstring if no short summary is present. I'm adding a I added the version to all methods (I used the date of the commit where the deprecation was added), and created a script to see all methods deprecated by version. There are some old ones that I think they should removed, but I think it makes more sense to label all them with the version in this pull request, and remove them in a later pull request, so it's easier to spot which methods have been removed. This is the list of deprecated methods in
Does all that sound good to you? |
If I'm not wrong, there is not documentation on what's the deprecation policy for Pandas, or how to contribute when classes, functions or methods are being deprecated.
There is this short note on the contributing guide: https://pandas.pydata.org/pandas-docs/stable/contributing.html#backwards-compatibility but I think some more information would be useful:
And not sure where, but in the user documentation, it would be useful to know what's the deprecation policy (e.g. adding a warning when it's marked for deprecation, and function being removed after one minor version).
Also, not sure if there is a reason I don't know, but in the code, it seems to be two different ways of documenting deprecations:
If this is just because of lack of consistency, it'd be good to unify them (I assume the docstring directive is the right way).
I'm happy to contribute a pull request once someone can clarify if I'm right, and what's the deprecation policy.
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